Isha Sood^*, Varsha Sharma

CMC-Computers, Materials & Continua, Vol.81, No.2, pp. 2791-2818, 2024, DOI:10.32604/cmc.2024.055463 - 18 November 2024

Abstract Ransomware has emerged as a critical cybersecurity threat, characterized by its ability to encrypt user data or lock devices, demanding ransom for their release. Traditional ransomware detection methods face limitations due to their assumption of similar data distributions between training and testing phases, rendering them less effective against evolving ransomware families. This paper introduces TLERAD (Transfer Learning for Enhanced Ransomware Attack Detection), a novel approach that leverages unsupervised transfer learning and co-clustering techniques to bridge the gap between source and target domains, enabling robust detection of both known and unknown ransomware variants. The proposed method More >

Laviza Falak Naz¹, Rohail Qamar^2,*, Raheela Asif¹, Muhammad Imran², Saad Ahmed³

Intelligent Automation & Soft Computing, Vol.39, No.5, pp. 855-887, 2024, DOI:10.32604/iasc.2024.055074 - 31 October 2024

Abstract Domain shift is when the data used in training does not match the ones it will be applied to later on under similar conditions. Domain shift will reduce accuracy in results. To prevent this, domain adaptation is done, which adapts the pre-trained model to the target domain. In real scenarios, the availability of labels for target data is rare thus resulting in unsupervised domain adaptation. Herein, we propose an innovative approach where source-free domain adaptation models and Generative Adversarial Networks (GANs) are integrated to improve the performance of computer vision or robotic vision-based systems in… More >

Byeongcheon Lee¹, Sangmin Kim¹, Muazzam Maqsood², Jihoon Moon^3,*, Seungmin Rho^1,4,*

CMC-Computers, Materials & Continua, Vol.81, No.1, pp. 1275-1300, 2024, DOI:10.32604/cmc.2024.054826 - 15 October 2024

Abstract In the context of rapid digitization in industrial environments, how effective are advanced unsupervised learning models, particularly hybrid autoencoder models, at detecting anomalies in industrial control system (ICS) datasets? This study is crucial because it addresses the challenge of identifying rare and complex anomalous patterns in the vast amounts of time series data generated by Internet of Things (IoT) devices, which can significantly improve the reliability and safety of these systems. In this paper, we propose a hybrid autoencoder model, called ConvBiLSTM-AE, which combines convolutional neural network (CNN) and bidirectional long short-term memory (BiLSTM) to More >

Tianyu Wang^1,2, Kongyang Chen^2,3,*

CMES-Computer Modeling in Engineering & Sciences, Vol.140, No.3, pp. 2297-2314, 2024, DOI:10.32604/cmes.2024.049964 - 08 July 2024

Abstract Crowdsourcing holds broad applications in information acquisition and dissemination, yet encounters challenges pertaining to data quality assessment and user reputation management. Reputation mechanisms stand as crucial solutions for appraising and updating participant reputation scores, thereby elevating the quality and dependability of crowdsourced data. However, these mechanisms face several challenges in traditional crowdsourcing systems: 1) platform security lacks robust guarantees and may be susceptible to attacks; 2) there exists a potential for large-scale privacy breaches; and 3) incentive mechanisms relying on reputation scores may encounter issues as reputation updates hinge on task demander evaluations, occasionally lacking… More >

Yanmei Shi¹, Wei Yu^2,*, Yanxia Zhao^3,*, Yungang Jia⁴

CMES-Computer Modeling in Engineering & Sciences, Vol.140, No.1, pp. 887-906, 2024, DOI:10.32604/cmes.2024.046140 - 16 April 2024

Abstract Web application fingerprint recognition is an effective security technology designed to identify and classify web applications, thereby enhancing the detection of potential threats and attacks. Traditional fingerprint recognition methods, which rely on preannotated feature matching, face inherent limitations due to the ever-evolving nature and diverse landscape of web applications. In response to these challenges, this work proposes an innovative web application fingerprint recognition method founded on clustering techniques. The method involves extensive data collection from the Tranco List, employing adjusted feature selection built upon Wappalyzer and noise reduction through truncated SVD dimensionality reduction. The core… More >

Ahmad Alassaf^*

Computer Systems Science and Engineering, Vol.47, No.3, pp. 2773-2789, 2023, DOI:10.32604/csse.2023.035899 - 09 November 2023

Abstract Intelligent diagnosis approaches with shallow architectural models play an essential role in healthcare. Deep Learning (DL) models with unsupervised learning concepts have been proposed because high-quality feature extraction and adequate labelled details significantly influence shallow models. On the other hand, skin lesion-based segregation and disintegration procedures play an essential role in earlier skin cancer detection. However, artefacts, an unclear boundary, poor contrast, and different lesion sizes make detection difficult. To address the issues in skin lesion diagnosis, this study creates the UDLS-DDOA model, an intelligent Unsupervised Deep Learning-based Stacked Auto-encoder (UDLS) optimized by Dynamic Differential… More >

Jingyao Liu^1,2, Qinghe Feng⁴, Jiashi Zhao^2,3, Yu Miao^2,3, Wei He², Weili Shi^2,3, Zhengang Jiang^2,3,*

CMC-Computers, Materials & Continua, Vol.76, No.3, pp. 2649-2665, 2023, DOI:10.32604/cmc.2023.038891 - 08 October 2023

Abstract The coronavirus disease 2019 (COVID-19) has severely disrupted both human life and the health care system. Timely diagnosis and treatment have become increasingly important; however, the distribution and size of lesions vary widely among individuals, making it challenging to accurately diagnose the disease. This study proposed a deep-learning disease diagnosis model based on weakly supervised learning and clustering visualization (W_CVNet) that fused classification with segmentation. First, the data were preprocessed. An optimizable weakly supervised segmentation preprocessing method (O-WSSPM) was used to remove redundant data and solve the category imbalance problem. Second, a deep-learning fusion method… More >

Iman Almomani^1,2, Walid El-Shafai^1,3,*, Aala AlKhayer¹, Albandari Alsumayt⁴, Sumayh S. Aljameel⁵, Khalid Alissa⁶

CMC-Computers, Materials & Continua, Vol.74, No.2, pp. 3515-3537, 2023, DOI:10.32604/cmc.2023.033765 - 31 October 2022

Abstract Nowadays, there is tremendous growth in biometric authentication and cybersecurity applications. Thus, the efficient way of storing and securing personal biometric patterns is mandatory in most governmental and private sectors. Therefore, designing and implementing robust security algorithms for users’ biometrics is still a hot research area to be investigated. This work presents a powerful biometric security system (BSS) to protect different biometric modalities such as faces, iris, and fingerprints. The proposed BSS model is based on hybridizing auto-encoder (AE) network and a chaos-based ciphering algorithm to cipher the details of the stored biometric patterns and… More >

Theerapat Sangpetch¹, Tossapon Boongoen^1,*, Natthakan Iam-On²

CMC-Computers, Materials & Continua, Vol.74, No.1, pp. 1641-1655, 2023, DOI:10.32604/cmc.2023.026739 - 22 September 2022

Abstract Attempts to determine characters of astronomical objects have been one of major and vibrant activities in both astronomy and data science fields. Instead of a manual inspection, various automated systems are invented to satisfy the need, including the classification of light curve profiles. A specific Kaggle competition, namely Photometric LSST Astronomical Time-Series Classification Challenge (PLAsTiCC), is launched to gather new ideas of tackling the abovementioned task using the data set collected from the Large Synoptic Survey Telescope (LSST) project. Almost all proposed methods fall into the supervised family with a common aim to categorize each… More >

P. Sherubha^1,*, S. P. Sasirekha², A. Dinesh Kumar Anguraj³, J. Vakula Rani⁴, Raju Anitha³, S. Phani Praveen^5,6, R. Hariharan Krishnan^5,6

Computer Systems Science and Engineering, Vol.45, No.1, pp. 149-166, 2023, DOI:10.32604/csse.2023.024424 - 16 August 2022

Abstract The Cloud system shows its growing functionalities in various industrial applications. The safety towards data transfer seems to be a threat where Network Intrusion Detection System (NIDS) is measured as an essential element to fulfill security. Recently, Machine Learning (ML) approaches have been used for the construction of intellectual IDS. Most IDS are based on ML techniques either as unsupervised or supervised. In supervised learning, NIDS is based on labeled data where it reduces the efficiency of the reduced model to identify attack patterns. Similarly, the unsupervised model fails to provide a satisfactory outcome. Hence,… More >